Commit f482da25 authored by matbuoro's avatar matbuoro
Browse files

Mise à jour estimation Oir

parent 9b6e251b
......@@ -18,7 +18,8 @@ stade <- "adult"
## WORKING DIRECTORY:
work.dir<-paste("/media/ORE/Abundance",site,stade,sep="/")
work.dir<-paste("~/Documents/RESEARCH/PROJECTS/ORE/Abundance",site,stade,sep="/")
#work.dir<-paste("/media/ORE/Abundance",site,stade,sep="/")
setwd(work.dir)
......@@ -32,21 +33,30 @@ source(paste('parameters_',stade,'.R',sep="")) # chargement des paramètres
#------------------------INITS----------------------------------##
source(paste('inits/inits_',stade,'.R',sep="")) # création des inits des données
load(paste('inits/inits_',stade,'.Rdata',sep="")) # chargement des inits
if(site == "Bresle" && stade == "adult") {inits <- list(read.bugsdata(paste("inits/init-",site,"-",stade,year,".txt",sep="")))}
if(site == "Nivelle") {inits <- list(read.bugsdata(paste("inits/init-",site,"-",stade,year,".txt",sep="")))}
#if(!file.exists(paste('inits/inits_',stade,year,'.Rdata',sep=""))){
if(!file.exists(paste("inits/init-",site,"-",stade,year,".txt",sep=""))){
source(paste('inits/inits_',stade,'.R',sep="")) # création des inits des données
#load(paste('inits/inits_',stade,year,'.Rdata',sep=""))
}
#load(paste('inits/inits_',stade,'.Rdata',sep="")) # chargement des inits
#if(site == "Bresle" && stade == "adult") {inits <- list(read.bugsdata(paste("inits/init-",site,"-",stade,year,".txt",sep="")))}
#if(site == "Nivelle") {inits <- list(read.bugsdata(paste("inits/init-",site,"-",stade,year,".txt",sep="")))}
inits <- list(read.bugsdata(paste("inits/init-",site,"-",stade,year,".txt",sep="")))
#------------------------MODEL----------------------------------##
model <- paste("model/",stade,"-",site,".R",sep="") # path of the model
model <- paste("model/model_",stade,"-",site,".R",sep="") # path of the model
if(site == "Scorff" && stade == "smolt") {model <- paste("model/",stade,"-",site,"_",year,".R",sep="")} # le modèle Scorrf pour les smolt peut changer tous les ans suivant conditions
model
filename <- file.path(work.dir, model)
#system(paste("cp",model,paste(stade,"-",site,".txt",sep=""),sep=""))
#---------------------------ANALYSIS-----------------------------##
nChains = length(inits) # Number of chains to run.
adaptSteps = 1000 # Number of steps to "tune" the samplers.
nburnin=5000 # Number of steps to "burn-in" the samplers.
nstore=50000 # Total number of steps in chains to save.
nstore=10000 # Total number of steps in chains to save.
nthin=1 # Number of steps to "thin" (1=keep every step).
#nPerChain = ceiling( ( numSavedSteps * thinSteps ) / nChains ) # Steps per chain.
......@@ -57,15 +67,25 @@ start.time = Sys.time(); cat("Start of the run\n");
fit <- bugs(
data
,inits
,model.file = model
,model.file = filename
,parameters
,n.chains = nChains, n.iter = nstore + nburnin, n.burnin = nburnin, n.thin = nthin
,DIC=FALSE
,codaPkg = FALSE, clearWD=TRUE
,codaPkg = FALSE, clearWD=FALSE
#,debug=TRUE
,working.directory=work.dir
,working.directory=paste(work.dir,"bugs",sep="/")
)
## cleaning
system("rm bugs/CODA*")
### Save inits ###
# save last values for inits
# inits <- fit$last.values
# if(site == "Nivelle") {
# save(inits,file=paste('inits/inits_',stade,year,'.Rdata',sep=""))
# }
######### JAGS ##########
## Compile & adapt
......
list(Nyears=3.30000E+01, Cm_R= structure(.Data= c(0.00000E+00, 1.00000E+01, 0.00000E+00, 2.00000E+00, 2.00000E+00, 1.30000E+01, 0.00000E+00, 6.00000E+00, 0.00000E+00, 5.00000E+00, 0.00000E+00, 0.00000E+00, 2.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 2.00000E+00, 9.00000E+00, 0.00000E+00, 1.00000E+00, 2.50000E+01, 1.10000E+01, 5.00000E+00, 3.00000E+00, 0.00000E+00, 1.00000E+00, 0.00000E+00, 1.00000E+00, 1.10000E+01, 1.00000E+01, 0.00000E+00, 1.00000E+00, 3.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 3.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 2.10000E+01, 1.50000E+01, 0.00000E+00, 0.00000E+00, 1.30000E+01, 6.00000E+00, 0.00000E+00, 0.00000E+00, 7.00000E+00, 4.00000E+00, 0.00000E+00, 0.00000E+00, 5.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 1.60000E+01, 6.00000E+00, 1.00000E+00, 0.00000E+00, 1.00000E+00, 3.00000E+00, 0.00000E+00, 0.00000E+00, 2.30000E+01, 5.00000E+00, 1.00000E+00, 1.00000E+00, 1.00000E+01, 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-8.47300E-01, -1.55800E+00, -1.94600E+00, -4.05500E-01, -1.09900E+00, -6.93100E-01, -5.10800E-01, 1.01200E+00, -6.93100E-01, 6.93100E-01, -1.32200E+00, -1.70500E+00, -6.93100E-01, -1.09900E+00, -2.84800E+00, -2.39800E+00, -1.60900E+00, -1.38600E+00, -2.56500E+00, -2.19700E+00, 0.00000E+00, -1.38600E+00, -6.70200E-01, -6.61400E-01, 0.00000E+00, -1.38600E+00, -6.93100E-01, -1.14500E+00, 0.00000E+00, -1.38600E+00, -1.25300E+00, -1.28100E+00, 0.00000E+00, -6.93100E-01, -1.15300E+00, -1.60900E+00, 0.00000E+00, -6.93100E-01, -1.52300E+00, -1.69200E+00, -9.16300E-01, -2.63900E+00, -2.48500E+00, -1.83300E+00, -6.93100E-01, -1.09900E+00, -1.17900E+00, -2.03700E+00, -6.93100E-01, -9.16300E-01, -1.38600E+00, -2.25100E+00, -1.25300E+00, -1.50400E+00, 1.90500E-02, -2.59500E-01, 6.93100E-01, -4.70000E-01, -9.41000E-01, -8.28700E-01, -1.09900E+00, -1.01200E+00, -4.75400E-01, -8.00400E-02, 0.00000E+00, -2.87700E-01, -8.39800E-01, -3.28500E-01, 0.00000E+00, -4.85500E-01, -3.50900E-02, -5.66400E-01, -6.93100E-01, -5.59600E-01, -9.16300E-01, -4.52000E-01, -6.93100E-01, 1.38600E+00, -1.09900E+00, -4.05500E-01, 0.00000E+00, -6.93100E-01, -8.36200E-01, -7.47200E-01, 0.00000E+00, -1.38600E+00, -3.71600E-01, -2.11000E+00, 0.00000E+00, -7.62100E-01, -6.19000E-01, -6.93100E-01, -1.38600E+00, -1.09900E+00, -6.24200E-01, -1.94600E+00, -6.93100E-01, -2.63900E+00, -8.10900E-01, 6.93100E-01, 0.00000E+00, -6.93100E-01, -1.82300E-01, -1.25300E+00, -6.93100E-01, 1.82300E-01, -4.59500E-01, 1.11200E-01, -6.93100E-01, -1.46600E+00), .Dim=c(33, 4)), n= structure(.Data= c(2.09000E+02, 8.50000E+01, 1.50000E+01, 4.70000E+01, 1.79000E+02, 9.10000E+01, 2.90000E+01, 7.00000E+01, 9.30000E+01, 2.30000E+01, 1.55000E+02, 1.30000E+02, 1.17000E+02, 2.10000E+01, 5.00000E+00, 5.00000E+00, 1.45000E+02, 4.10000E+01, 2.90000E+01, 1.31000E+02, 1.44000E+02, 7.20000E+01, 2.70000E+01, 3.00000E+01, 5.60000E+01, 3.00000E+01, 1.10000E+01, 1.70000E+01, 3.90000E+01, 1.90000E+01, 7.00000E+00, 7.00000E+00, 3.50000E+01, 2.40000E+01, 1.00000E+01, 1.50000E+01, 1.32000E+02, 4.40000E+01, 2.10000E+01, 1.70000E+01, 4.70000E+01, 6.80000E+01, 5.00000E+00, 1.70000E+01, 9.10000E+01, 6.70000E+01, 5.00000E+00, 1.40000E+01, 1.68000E+02, 1.15000E+02, 5.00000E+00, 1.40000E+01, 5.00000E+01, 3.30000E+01, 5.00000E+00, 8.00000E+00, 1.23000E+02, 5.60000E+01, 5.00000E+00, 1.30000E+01, 1.54000E+02, 7.40000E+01, 1.70000E+01, 3.70000E+01, 1.82000E+02, 2.04000E+02, 1.10000E+01, 1.70000E+01, 1.29000E+02, 6.70000E+01, 1.10000E+01, 1.30000E+01, 2.01000E+02, 1.53000E+02, 2.30000E+01, 2.90000E+01, 1.22000E+02, 7.30000E+01, 6.00000E+00, 3.70000E+01, 1.82000E+02, 1.63000E+02, 1.90000E+01, 7.30000E+01, 1.03000E+02, 4.40000E+01, 9.00000E+00, 1.60000E+01, 1.10000E+02, 7.50000E+01, 5.00000E+00, 4.30000E+01, 1.28000E+02, 6.60000E+01, 7.00000E+00, 2.70000E+01, 3.90000E+01, 5.10000E+01, 7.00000E+00, 1.30000E+01, 9.40000E+01, 3.10000E+01, 5.00000E+00, 2.80000E+01, 3.56000E+02, 2.27000E+02, 5.00000E+00, 2.60000E+01, 1.25000E+02, 9.40000E+01, 1.00000E+01, 6.20000E+01, 1.40000E+02, 3.50000E+01, 2.60000E+01, 4.70000E+01, 2.20000E+02, 7.80000E+01, 3.10000E+01, 8.90000E+01, 1.35000E+02, 5.20000E+01, 5.00000E+00, 4.30000E+01, 6.30000E+01, 5.00000E+01, 8.00000E+00, 3.60000E+01, 1.76000E+02, 1.02000E+02, 7.00000E+00, 4.20000E+01), .Dim=c(33, 4)))
This diff is collapsed.
modelCheck('/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance/Oir/adult/bugs/model_adult-Oir.R.txt')
modelData('/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance/Oir/adult/bugs/data.txt')
modelCompile(1)
modelSetRN(1)
modelInits('/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance/Oir/adult/bugs/inits1.txt',1)
modelGenInits()
modelUpdate(5000,1,5000)
samplesSet(logit_int_MC)
samplesSet(logit_flow_MC)
samplesSet(sigmap_eff)
samplesSet(shape_lambda)
samplesSet(rate_lambda)
samplesSet(s)
samplesSet(Plambda)
samplesSet(Plambda0)
samplesSet(mup_recap)
samplesSet(sigmap_recap)
samplesSet(pi_MC)
samplesSet(p_MC90)
samplesSet(epsilon_MC)
samplesSet(p_MC90_1SW)
samplesSet(p_MC90_MSW)
samplesSet(p_recap)
samplesSet(n_tot)
samplesSet(n_1SW)
samplesSet(n_MSW)
samplesSet(n)
samplesSet(lambda0)
samplesSet(lambda_n)
samplesSet(lambda)
samplesSet(Nesc)
samplesSet(Nesc_1SW)
samplesSet(Nesc_MSW)
samplesSet(Nesc_tot)
samplesSet(test)
summarySet(logit_int_MC)
summarySet(logit_flow_MC)
summarySet(sigmap_eff)
summarySet(shape_lambda)
summarySet(rate_lambda)
summarySet(s)
summarySet(Plambda)
summarySet(Plambda0)
summarySet(mup_recap)
summarySet(sigmap_recap)
summarySet(pi_MC)
summarySet(p_MC90)
summarySet(epsilon_MC)
summarySet(p_MC90_1SW)
summarySet(p_MC90_MSW)
summarySet(p_recap)
summarySet(n_tot)
summarySet(n_1SW)
summarySet(n_MSW)
summarySet(n)
summarySet(lambda0)
summarySet(lambda_n)
summarySet(lambda)
summarySet(Nesc)
summarySet(Nesc_1SW)
summarySet(Nesc_MSW)
summarySet(Nesc_tot)
summarySet(test)
modelUpdate(10000,1,10000)
samplesCoda('*', '/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance/Oir/adult/bugs//')
summaryStats('*')
modelQuit('y')
......@@ -35,4 +35,5 @@ C_MC[,1] C_MC[,2] C_MC[,3] C_MC[,4] Cm_MC[,1] Cm_MC[,2] Cm_MC[,3] Cm_MC[,4]
41 14 4 13 38 13 4 13
11 4 0 4 11 4 0 4
9 7 1 9 9 7 1 9
60 34 1 14 60 34 1 14
#END
\ No newline at end of file
......@@ -35,4 +35,5 @@ Cm_R[,1] Cm_R[,2] Cm_R[,3] Cm_R[,4] Cum_R[,1] Cum_R[,2] Cum_R[,3] Cum_R[,4]
14 1 1 0 49 17 1 8
3 3 0 1 45 29 2 13
4 1 0 5 20 11 2 11
23 18 0 2 55 21 0 3
#END
......@@ -32,7 +32,7 @@ Q = c(209.3,
1688.583333,
1077.717949,
995.7283951,
856.6759259)
856.6759259,
637.5384615)
)
# Number of years: from 1984 to now on
list(Nyears = 32)
# Number of years: from 1984 to now on
list(Nyears = 33)
list(sigmap_eff=c(1.000E-01, 2.000E-01), shape_lambda=5.000E+00, rate_lambda=1.000E-02, lambda0=5.000E+02, mup_recap=c(3.000E-01, 5.000E-01, 3.000E-01, 5.000E-01), sigmap_recap=c(1.000E-01, 1.000E-01, 2.000E-01, 3.000E-01), lambda=c(5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02, 5.000E+02), logit_pi_eff= structure(.Data= c(0.000E+00, 0.000E+00, 1.163E+00, 8.473E-01, 6.931E-01, -1.386E+00, -1.946E+00, -1.609E+00, 1.792E+00, 0.000E+00, 2.918E+00, 1.504E+00, -2.015E+00, -6.931E-01, 1.992E+00, 0.000E+00, 2.231E-01, -1.386E+00, 2.231E-01, -1.099E+00, -9.163E-01, -1.099E+00, 9.719E-01, -6.931E-01, -1.131E+00, -6.931E-01, 1.099E+00, -6.931E-01, -1.421E+00, -1.946E+00, 4.964E-01, -4.055E-01, -1.856E+00, -1.609E+00, 1.421E+00, 4.055E-01, -9.985E-01, -5.108E-01, 1.972E+00, 1.012E+00, 5.436E-01, -3.677E-01, 3.483E-01, -2.231E-01, 3.254E-01, 0.000E+00, 2.251E+00, -4.055E-01, -6.419E-01, -5.596E-01, -2.079E+00, -8.473E-01, -2.024E+00, -1.792E+00, -4.480E-01, -6.286E-01, -1.872E+00, -1.792E+00, -1.447E+00, -1.705E+00, -2.385E+00, -2.140E+00, -1.705E+00, -9.163E-01, -6.190E-01, -5.108E-01), .Dim=c(33, 2)), logit_p_recap= structure(.Data= c(-4.635E+00, -1.036E+00, -1.792E+00, -1.897E+00, -3.784E+00, -1.350E+00, -2.890E+00, -1.768E+00, -4.094E+00, -2.877E-01, -3.434E+00, -3.258E+00, -1.466E+00, -1.099E+00, 0.000E+00, 0.000E+00, -3.681E+00, -8.329E-01, -2.565E+00, -3.450E+00, -1.424E+00, -1.504E+00, -8.473E-01, -1.558E+00, -1.946E+00, -4.055E-01, -1.099E+00, -6.931E-01, -5.108E-01, 1.012E+00, -6.931E-01, 6.931E-01, -1.322E+00, -1.705E+00, -6.931E-01, -1.099E+00, -2.848E+00, -2.398E+00, -1.609E+00, -1.386E+00, -2.565E+00, -2.197E+00, 0.000E+00, -1.386E+00, -6.702E-01, -6.614E-01, 0.000E+00, -1.386E+00, -6.931E-01, -1.145E+00, 0.000E+00, -1.386E+00, -1.253E+00, -1.281E+00, 0.000E+00, -6.931E-01, -1.153E+00, -1.609E+00, 0.000E+00, -6.931E-01, -1.523E+00, -1.692E+00, -9.163E-01, -2.639E+00, -2.485E+00, -1.833E+00, -6.931E-01, -1.099E+00, -1.179E+00, -2.037E+00, -6.931E-01, -9.163E-01, -1.386E+00, -2.251E+00, -1.253E+00, -1.504E+00, 1.905E-02, -2.595E-01, 6.931E-01, -4.700E-01, -9.410E-01, -8.287E-01, -1.099E+00, -1.012E+00, -4.754E-01, -8.004E-02, 0.000E+00, -2.877E-01, -8.398E-01, -3.285E-01, 0.000E+00, -4.855E-01, -3.509E-02, -5.664E-01, -6.931E-01, -5.596E-01, -9.163E-01, -4.520E-01, -6.931E-01, 1.386E+00, -1.099E+00, -4.055E-01, 0.000E+00, -6.931E-01, -8.362E-01, -7.472E-01, 0.000E+00, -1.386E+00, -3.716E-01, -2.110E+00, 0.000E+00, -7.621E-01, -6.190E-01, -6.931E-01, -1.386E+00, -1.099E+00, -6.242E-01, -1.946E+00, -6.931E-01, -2.639E+00, -8.109E-01, 6.931E-01, 0.000E+00, -6.931E-01, -1.823E-01, -1.253E+00, -6.931E-01, 1.823E-01, -4.595E-01, 1.112E-01, -6.931E-01, -1.466E+00), .Dim=c(33, 4)), n= structure(.Data= c(2.090E+02, 8.500E+01, 1.500E+01, 4.700E+01, 1.790E+02, 9.100E+01, 2.900E+01, 7.000E+01, 9.300E+01, 2.300E+01, 1.550E+02, 1.300E+02, 1.170E+02, 2.100E+01, 5.000E+00, 5.000E+00, 1.450E+02, 4.100E+01, 2.900E+01, 1.310E+02, 1.440E+02, 7.200E+01, 2.700E+01, 3.000E+01, 5.600E+01, 3.000E+01, 1.100E+01, 1.700E+01, 3.900E+01, 1.900E+01, 7.000E+00, 7.000E+00, 3.500E+01, 2.400E+01, 1.000E+01, 1.500E+01, 1.320E+02, 4.400E+01, 2.100E+01, 1.700E+01, 4.700E+01, 6.800E+01, 5.000E+00, 1.700E+01, 9.100E+01, 6.700E+01, 5.000E+00, 1.400E+01, 1.680E+02, 1.150E+02, 5.000E+00, 1.400E+01, 5.000E+01, 3.300E+01, 5.000E+00, 8.000E+00, 1.230E+02, 5.600E+01, 5.000E+00, 1.300E+01, 1.540E+02, 7.400E+01, 1.700E+01, 3.700E+01, 1.820E+02, 2.040E+02, 1.100E+01, 1.700E+01, 1.290E+02, 6.700E+01, 1.100E+01, 1.300E+01, 2.010E+02, 1.530E+02, 2.300E+01, 2.900E+01, 1.220E+02, 7.300E+01, 6.000E+00, 3.700E+01, 1.820E+02, 1.630E+02, 1.900E+01, 7.300E+01, 1.030E+02, 4.400E+01, 9.000E+00, 1.600E+01, 1.100E+02, 7.500E+01, 5.000E+00, 4.300E+01, 1.280E+02, 6.600E+01, 7.000E+00, 2.700E+01, 3.900E+01, 5.100E+01, 7.000E+00, 1.300E+01, 9.400E+01, 3.100E+01, 5.000E+00, 2.800E+01, 3.560E+02, 2.270E+02, 5.000E+00, 2.600E+01, 1.250E+02, 9.400E+01, 1.000E+01, 6.200E+01, 1.400E+02, 3.500E+01, 2.600E+01, 4.700E+01, 2.200E+02, 7.800E+01, 3.100E+01, 8.900E+01, 1.350E+02, 5.200E+01, 5.000E+00, 4.300E+01, 6.300E+01, 5.000E+01, 8.000E+00, 3.600E+01, 1.760E+02, 1.020E+02, 7.000E+00, 4.200E+01), .Dim=c(33, 4)))
......@@ -3,6 +3,10 @@
# site <- "Oir"
# stade <- "adult"
##-----------------------------FUNCTIONS ----------------------------------##
invlogit<-function(x) {1/(1+exp(-(x)))}
logit<-function(x) {log(x/(1-x))}
load(paste('data/data_',stade,"_",year,'.Rdata',sep=""))
......@@ -26,136 +30,147 @@ inits_fix <- list(
###################################################
# TO UPDATE
###################################################
inits_updated <- list(
#inits_updated <- list(
# METTRE A JOUR
lambda=c(
500,500,500,500,500,500,500,500,500,500,500,500,500,500,500,500,500,500,500,
500,500,500,500,500,500,500,500,500,500,500,500,500),
# lambda=c(
# 500,500,500,500,500,500,500,500,500,500,500,500,500,500,500,500,500,500,500,
# 500,500,500,500,500,500,500,500,500,500,500,500,500),
lambda = rep(500, data$Nyears)
# METTRE A JOUR /!\ TAILLE MATRICE
logit_pi_eff = structure(.Data = c(
0.5127265, -0.50516446,
0.2429615, -0.29816470,
0.4358002, -0.44999789,
0.4581223, -0.24020537,
0.4148411, -0.34124070,
0.4081585, -0.06255602,
0.3163549, -0.21218071,
0.3692032, -0.49684217,
0.5685715, -0.10636175,
0.4352412, -0.66264032,
0.3243872, -0.43484468,
0.5369755, -0.61989005,
0.4781544, -0.31793986,
0.4415721, -0.44393003,
0.5809606, -0.57391739,
0.4627385, -0.73267519,
0.5533487, -0.29324114,
0.6357414, -0.60006038,
0.4227797, -0.16913003,
0.2927949, -0.15004335,
0.3602127, -0.28234524,
0.3725753, -0.28091169,
0.3025387, -0.45968826,
0.2403897, -0.57003221,
0.4262693, -0.42518736,
0.2536607, -0.14655583,
0.5111182, -0.42747081,
0.4580112, -0.52691562,
0.4590758, -0.56539226,
0.4859690, -0.53768309,
0.4590758, -0.56539226,
0.4859690, -0.53768309),
.Dim = c(32,2)),
# annual probability to be captured at Moulin de Cerisel per sea age at logit scale
# logit_pi_eff = structure(.Data = c(
# 0.5127265, -0.50516446,
# 0.2429615, -0.29816470,
# 0.4358002, -0.44999789,
# 0.4581223, -0.24020537,
# 0.4148411, -0.34124070,
# 0.4081585, -0.06255602,
# 0.3163549, -0.21218071,
# 0.3692032, -0.49684217,
# 0.5685715, -0.10636175,
# 0.4352412, -0.66264032,
# 0.3243872, -0.43484468,
# 0.5369755, -0.61989005,
# 0.4781544, -0.31793986,
# 0.4415721, -0.44393003,
# 0.5809606, -0.57391739,
# 0.4627385, -0.73267519,
# 0.5533487, -0.29324114,
# 0.6357414, -0.60006038,
# 0.4227797, -0.16913003,
# 0.2927949, -0.15004335,
# 0.3602127, -0.28234524,
# 0.3725753, -0.28091169,
# 0.3025387, -0.45968826,
# 0.2403897, -0.57003221,
# 0.4262693, -0.42518736,
# 0.2536607, -0.14655583,
# 0.5111182, -0.42747081,
# 0.4580112, -0.52691562,
# 0.4590758, -0.56539226,
# 0.4859690, -0.53768309,
# 0.4590758, -0.56539226,
# 0.4859690, -0.53768309),
# .Dim = c(32,2)),
logit_pi_eff <- cbind(
logit( (data$Cm_R[,1]+data$Cm_R[,2]+1) / ((data$Cm_R[,1]+data$Cm_R[,2]+1)+(data$Cum_R[,1]+data$Cum_R[,2]+2))) ,
logit( (data$Cm_R[,3]+data$Cm_R[,4]+1) / ((data$Cm_R[,3]+data$Cm_R[,4]+1)+(data$Cum_R[,3]+data$Cum_R[,4]+2)))
)
#logit_pi_eff <- ifelse(logit_pi_eff=="-Inf",NA,logit_pi_eff)
# METTRE A JOUR /!\ TAILLE MATRICE
logit_p_recap = structure(.Data = c(
-0.7899411, -0.119189383,-0.7899411, -0.119189383,
-1.0105426, 0.112161640,-1.0105426, 0.112161640,
-0.6650540, 0.095865979,-0.6650540, 0.095865979,
-0.9352202, 0.153063054,-0.9352202, 0.153063054,
-0.8925006, 0.003701361,-0.8925006, 0.003701361,
-0.7584533, 0.162499740,-0.7584533, 0.162499740,
-0.8783464, 0.015433624,-0.8783464, 0.015433624,
-0.9635229, 0.037277000,-0.9635229, 0.037277000,
-0.7301189, 0.032563762,-0.7301189, 0.032563762,
-0.7242909, -0.203297658,-0.7242909, -0.203297658,
-1.0152822, -0.059872607,-1.0152822, -0.059872607,
-0.8991247, -0.062853404,-0.8991247, -0.062853404,
-0.8910851, 0.148828121,-0.8910851, 0.148828121,
-0.8421063, -0.036137567,-0.8421063, -0.036137567,
-0.8724257, -0.127640120,-0.8724257, -0.127640120,
-0.8241181, 0.081526148,-0.8241181, 0.081526148,
-0.9301552, 0.103002530,-0.9301552, 0.103002530,
-0.9322924, 0.054081970,-0.9322924, 0.054081970,
-0.8599834, -0.002053284,-0.8599834, -0.002053284,
-0.9211022, 0.198061863,-0.9211022, 0.198061863,
-0.8012836, 0.015605389,-0.8012836, 0.015605389,
-0.8367525, 0.020028270,-0.8367525, 0.020028270,
-0.9473647, -0.121825226,-0.9473647, -0.121825226,
-0.7446897, 0.083201546,-0.7446897, 0.083201546,
-0.9311430, -0.088221582,-0.9311430, -0.088221582,
-0.6837008, -0.057448455,-0.6837008, -0.057448455,
-0.8025545, 0.155888834,-0.8025545, 0.155888834,
-0.9052695, -0.038361727,-0.9052695, -0.038361727,
-0.8656477, -0.040313032,-0.8656477, -0.040313032,
-0.9537507, 0.041536211,-0.9537507, 0.041536211,
-0.8656477, -0.040313032,-0.8656477, -0.040313032,
-0.9537507, 0.041536211,-0.9537507, 0.041536211),
.Dim = c(32,4)),
# logit_p_recap = structure(.Data = c(
# -0.7899411, -0.119189383,-0.7899411, -0.119189383,
# -1.0105426, 0.112161640,-1.0105426, 0.112161640,
# -0.6650540, 0.095865979,-0.6650540, 0.095865979,
# -0.9352202, 0.153063054,-0.9352202, 0.153063054,
# -0.8925006, 0.003701361,-0.8925006, 0.003701361,
# -0.7584533, 0.162499740,-0.7584533, 0.162499740,
# -0.8783464, 0.015433624,-0.8783464, 0.015433624,
# -0.9635229, 0.037277000,-0.9635229, 0.037277000,
# -0.7301189, 0.032563762,-0.7301189, 0.032563762,
# -0.7242909, -0.203297658,-0.7242909, -0.203297658,
# -1.0152822, -0.059872607,-1.0152822, -0.059872607,
# -0.8991247, -0.062853404,-0.8991247, -0.062853404,
# -0.8910851, 0.148828121,-0.8910851, 0.148828121,
# -0.8421063, -0.036137567,-0.8421063, -0.036137567,
# -0.8724257, -0.127640120,-0.8724257, -0.127640120,
# -0.8241181, 0.081526148,-0.8241181, 0.081526148,
# -0.9301552, 0.103002530,-0.9301552, 0.103002530,
# -0.9322924, 0.054081970,-0.9322924, 0.054081970,
# -0.8599834, -0.002053284,-0.8599834, -0.002053284,
# -0.9211022, 0.198061863,-0.9211022, 0.198061863,
# -0.8012836, 0.015605389,-0.8012836, 0.015605389,
# -0.8367525, 0.020028270,-0.8367525, 0.020028270,
# -0.9473647, -0.121825226,-0.9473647, -0.121825226,
# -0.7446897, 0.083201546,-0.7446897, 0.083201546,
# -0.9311430, -0.088221582,-0.9311430, -0.088221582,
# -0.6837008, -0.057448455,-0.6837008, -0.057448455,
# -0.8025545, 0.155888834,-0.8025545, 0.155888834,
# -0.9052695, -0.038361727,-0.9052695, -0.038361727,
# -0.8656477, -0.040313032,-0.8656477, -0.040313032,
# -0.9537507, 0.041536211,-0.9537507, 0.041536211,
# -0.8656477, -0.040313032,-0.8656477, -0.040313032,
# -0.9537507, 0.041536211,-0.9537507, 0.041536211),
# .Dim = c(32,4)),
logit_p_recap = logit((data$Cm_R+1) / (data$C_MC+2))
# METTRE A JOUR /!\ TAILLE MATRICE
n=structure(.Data = c(
602,540,505,521,
632,566,517,546,
559,512,530,525,
514,502,500,500,
620,531,512,563,
632,564,518,521,
506,503,502,504,
530,513,501,501,
517,511,501,502,
571,522,504,503,
512,518,500,503,
563,545,500,503,
540,527,500,503,
534,521,500,501,
523,510,500,501,
593,543,505,513,
524,527,501,502,
600,550,504,505,
553,540,507,508,
603,560,501,524,
612,600,506,528,
558,523,502,505,
561,541,500,519,
612,556,501,509,
512,516,501,503,
510,503,500,507,
541,526,500,503,
547,535,502,520,
518,504,503,506,
541,514,504,513,
518,504,503,506,
541,514,504,513),
.Dim = c(32,4))
# inits_updated <- list(
# lambda_tot = lambda_tot
# ,logit_piD_1SW=logit_piD_1SW
# ,logit_piD_MSW=logit_piD_MSW
# ,logit_piF_1SW=logit_piF_1SW
# ,logit_piF_MSW=logit_piF_MSW
# ,logit_pi_MP=logit_pi_MP
# ,logit_pi_R=logit_pi_R
# ,m_D=m_D
# ,n=n
# ,um_D=um_D
# )
# n=structure(.Data = c(
# 602,540,505,521,
# 632,566,517,546,
# 559,512,530,525,
# 514,502,500,500,
# 620,531,512,563,
# 632,564,518,521,
# 506,503,502,504,
# 530,513,501,501,
# 517,511,501,502,
# 571,522,504,503,
# 512,518,500,503,
# 563,545,500,503,
# 540,527,500,503,
# 534,521,500,501,
# 523,510,500,501,
# 593,543,505,513,
# 524,527,501,502,
# 600,550,504,505,
# 553,540,507,508,
# 603,560,501,524,
# 612,600,506,528,
# 558,523,502,505,
# 561,541,500,519,
# 612,556,501,509,
# 512,516,501,503,
# 510,503,500,507,
# 541,526,500,503,
# 547,535,502,520,
# 518,504,503,506,
# 541,514,504,513,
# 518,504,503,506,
# 541,514,504,513),
# .Dim = c(32,4))
n <- array(,dim=c(data$Nyears,4))
n[,1] = as.integer((data$C_MC[,1]/invlogit(logit_pi_eff[,1])))+ 5
n[,2] = as.integer((data$C_MC[,2]/invlogit(logit_pi_eff[,1])))+ 5
n[,3] = as.integer((data$C_MC[,3]/invlogit(logit_pi_eff[,2])))+ 5
n[,4] = as.integer((data$C_MC[,4]/invlogit(logit_pi_eff[,2])))+ 5
inits_updated <- list(
lambda = lambda
,logit_pi_eff=logit_pi_eff
,logit_p_recap=logit_p_recap
,n=n
)
inits <- list(c( inits_fix,inits_updated))
save(inits,file=paste(paste('inits/inits_',stade,'.Rdata',sep="")))
#save(inits,file=paste('inits/inits_',stade,year,'.Rdata',sep=""))
bugs.inits(inits, n.chains=1,digits=3, inits.files = paste('inits/init-',site,'-',stade,year,'.txt',sep=""))
=============================
DIAGNOSTICS
=============================
---------------------------
Heidelberger and Welch's convergence diagnostic
heidel.diag is a run length control diagnostic based on a criterion of relative accuracy for the estimate of the mean. The default setting corresponds to a relative accuracy of two significant digits.
heidel.diag also implements a convergence diagnostic, and removes up to half the chain in order to ensure that the means are estimated from a chain that has converged.
Stationarity start p-value
test iteration
shape_lambda passed 1 0.298
rate_lambda passed 1 0.224
p_MC90_1SW passed 1 0.848
p_MC90_MSW passed 1 0.596
lambda0 passed 1 0.310
Halfwidth Mean Halfwidth
test
shape_lambda passed 3.7786 0.089681
rate_lambda passed 0.0172 0.000451
p_MC90_1SW passed 0.1253 0.005723
p_MC90_MSW passed 0.2955 0.014528
lambda0 passed 221.5138 7.184882
---------------------------
Geweke's convergence diagnostic
Geweke (1992) proposed a convergence diagnostic for Markov chains based on a test for equality of the means of the first and last part of a Markov chain (by default the first 10% and the last 50%).
If the samples are drawn from the stationary distribution of the chain, the two means are equal and Geweke's statistic has an asymptotically standard normal distribution.
The test statistic is a standard Z-score: the difference between the two sample means divided by its estimated standard error. The standard error is estimated from the spectral density at zero and so takes into account any autocorrelation.
The Z-score is calculated under the assumption that the two parts of the chain are asymptotically independent, which requires that the sum of frac1 and frac2 be strictly less than 1.
Fraction in 1st window = 0.1
Fraction in 2nd window = 0.5
shape_lambda rate_lambda p_MC90_1SW p_MC90_MSW lambda0
0.492 0.258 0.541 0.800 -0.964
---------------------------
Raftery and Lewis's diagnostic
Quantile (q) = 0.025
Accuracy (r) = +/- 0.005
Probability (s) = 0.95
Burn-in Total Lower bound Dependence
(M) (N) (Nmin) factor (I)
shape_lambda 18 20262 3746 5.41
rate_lambda 16 16250 3746 4.34
p_MC90_1SW 18 19480 3746 5.20
p_MC90_MSW 20 27940 3746 7.46
lambda0 15 14655 3746 3.91
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